Quantity Does Not Always Mean Quality: The Importance of Qualitative Social Science in Conservation Research
Bibliographic record
Abstract
Qualitative methods are important to gain a deep understanding of complex problems and poorly researched areas. They can be particularly useful to help explain underlying conservation problems. However, the significance in choosing and justifying appropriate methodological frameworks in conservation studies should be given more attention to ensure data are collected and analysed appropriately. We explain when, why, and how qualitative methods should be used and explain sampling strategies in qualitative studies. To improve familiarity with qualitative methods among natural scientists, we recommend expanding training in social sciences and increasing collaboration with social scientists. Given the scale of human impacts on the environment, this type of nuanced analytical skill is critical for progressing biodiversity conservation efforts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.435 | 0.543 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.012 | 0.069 |
| Scholarly communication | 0.024 | 0.035 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".